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	<h1>Frequency weighting to reduce the computational burden</h1>
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                     Code: <a href="binomial_w2.tpl">binomial_w2.tpl</a><br>
                     Data: 		<a href="binomial_w2.dat">binomial_w2.dat</a><br>
                     Initial values: <a href="binomial_w2.pin">binomial_w2.pin</a><br>
                     Expected Results: <a href="binomial_w2.par">binomial_w2-expected-results.par</a><br>
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<h3><strong>Model description</strong></h3>

For categorical data with a small number of possible outcomes it is often possible use
frequency weighting to reduce the computational burden. The basic idea is that you
only have to evaluate the likelihood once for each outcome value, not once for each
observation. Hence there is no limit to the number of observations you can handle, as long as the
number outcome categories stays fixed. Further details are given in following <A href="binomial_w2.pdf">example</A>



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